05-lincs.Rmd

# LINCS

This is LINCS

## Signatures

Putative drug therapies were identified with signature-based connectivity analysis utilizing the Library of Integrated Network-based Signatures (LINCS) database.

```{r, results='asis'}
#Input: Annotated DrugFindr investigate signature results
generate_moa_report <- function(X) {
  X %>%
    select(integratedMoas, Target, GeneTargets) %>%
    filter(integratedMoas != "" & !is.na(integratedMoas)) %>%
    separate_rows(integratedMoas, sep = "\\|") %>%
    separate_rows(GeneTargets, sep = "\\|") %>%
    mutate(across(c(integratedMoas, GeneTargets), str_trim)) %>%
    group_by(integratedMoas) %>%
    summarise(
      Target = paste(unique(Target), collapse = "|"),
      GeneTargets = paste(unique(GeneTargets), collapse = "|"),
      N = n()
    ) %>%
    arrange(desc(N))
}

#Input: Annotated DrugFindr investigate signature results | DEG data
generate_gene_report <- function(X, data) {
  X %>%
    select(Symbol = GeneTargets) %>%
    filter(Symbol != "" & !is.na(Symbol)) %>%
    separate_rows(Symbol, sep = "\\|") %>%
    mutate(Symbol = str_trim(Symbol)) %>%
    count(Symbol) %>%
    arrange(desc(n)) %>%
    inner_join(global_state$hgnc, by = "Symbol") %>%
    inner_join(data, by = "Symbol")
}

#Input: DEG signature
#Output: Named list of relevant LINCS results
get_ilincs_results <- function(X) {
  if(global_state$species != "human") { #convert mouse or rat orthologs to human genes for lincs
      orthologs <- orthologs(X$Symbol, human = F, species = global_state$species) %>%
        select(Symbol = human_symbol, Symbol_old = symbol)
      
      X <- X %>%
        rename(Symbol_old = Symbol) %>%
        inner_join(orthologs, by = "Symbol_old") %>%
        select(-Symbol_old)
  }
  
  signatures <- investigateSignature(
    X,
    filterThreshold = 0,
    outputLib = "CP",
    geneColumn = "Symbol",
    logfcColumn = "log2FoldChange",
    pvalColumn = "pvalue"
  )
  
  concordant <- signatures %>%
    filter(Similarity >= 0) %>% inner_join(get_ilincs_metadata(.$TargetSignature), by = "TargetSignature") %>%
    left_join(global_state$lincs_fda, by = c("Target" = "sm_name"))
    
  discordant <- signatures %>%
    filter(Similarity < 0) %>% inner_join(get_ilincs_metadata(.$TargetSignature), by = "TargetSignature") %>%
    left_join(global_state$lincs_fda, by = c("Target" = "sm_name"))
  
  concordant_moa_report <- generate_moa_report(concordant)
  discordant_moa_report <- generate_moa_report(discordant)
  
  concordant_gene_report <- generate_gene_report(concordant, X)
  discordant_gene_report <- generate_gene_report(discordant, X)
  
  discordant_pathways <- do_enrichr(discordant_gene_report$Symbol) 
  system("sleep 3")
  concordant_pathways <- do_enrichr(concordant_gene_report$Symbol)
  
  lst(concordant,
    discordant,
    concordant_moa_report,
    discordant_moa_report,
    concordant_gene_report,
    discordant_gene_report,
    discordant_pathways,
    concordant_pathways)
}

global_state$data %<>%
  map(~ update_list(., results = c(.$results, lincs = list(get_ilincs_results(.$data)))))

global_state$data %>%
  map(~ knit_child("Rmd/lincs_signatures.Rmd", envir = environment(), quiet = TRUE)) %>%
  list_c() %>%
  cat(sep = "\n")
```

## MOAS and Genes

These are LINCS Perturbagens MoAs and Genetargets

```{r, results='asis'}
global_state$data %>%
  map(~ knit_child("Rmd/lincs_metadata.Rmd", envir = environment(), quiet = TRUE)) %>%
  list_c() %>%
  cat(sep = "\n")
```

## Pathways

These are LINCS Pathways

```{r, results='asis'}
global_state$data %>%
  map(~ knit_child("Rmd/lincs_pathways.Rmd", envir = environment(), quiet = TRUE)) %>%
  list_c() %>%
  cat(sep = "\n")
```

## PAVER

These are PAVER plots of the pathways from all comparisons

```{r, results='asis'}
input = global_state$data %>%
  map(~ bind_rows(.$results$lincs$concordant_pathways, .$results$lincs$discordant_pathways %>% mutate(Combined.Score = Combined.Score * -1))) %>%
  bind_rows(.id = "Group") %>%
  select(GOID, CS = Combined.Score, Group) %>%
  mutate(CS = sign(CS) * log2(abs(CS)) + 1) %>%
  mutate(Type = ifelse(CS > 0, "C", "D")) %>%
  pivot_wider(names_from = c(Group, Type), values_from = CS, names_sep = "-")

minClusterSize = 5
maxCoreScatter = 0.33
minGap = (1 - maxCoreScatter) * 3 / 4

LINCS_PAVER_result <- quiet(PAVER::prepare_data(input, global_state$embeddings, global_state$term2name) %>%
  PAVER::generate_themes(maxCoreScatter = maxCoreScatter,
  minGap = minGap,
  minClusterSize = minClusterSize))

global_state$results <- c(global_state$results, lst(LINCS_PAVER_result))

list(LINCS_PAVER_result, "logCS") %>%
  knit_child(text = readLines("Rmd/paver_report.Rmd"),
             envir = environment(),
             quiet = TRUE) %>%
  cat(sep = "\n")
```